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Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping

Author

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  • Hardy, Mathew D.
  • Zhang, Sam
  • Hullman, Jessica
  • Hofman, Jake M.
  • Goldstein, Daniel G.

Abstract

We propose and test a method for out-of-population prediction termed model-assisted judgmental bootstrapping, which leverages a predictive model from one domain combined with expert judgment to generate training data and subsequently a predictive model for a new domain. In a preregistered experiment (N=1440), we assessed the predictive accuracy of this method in increasingly challenging environments. We also analyzed the individual contributions of two techniques that underlie the method: model-assisted estimation and judgmental bootstrapping. Our findings revealed that both techniques significantly improved predictive accuracy. Furthermore, their impacts were complementary: model-assisted estimation provided the largest accuracy gains in the least demanding environment, while judgmental bootstrapping did so in the most challenging environment. Our results suggest that model-assisted judgmental bootstrapping is a promising technique for creating predictive models in domains in which outcome data are not available.

Suggested Citation

  • Hardy, Mathew D. & Zhang, Sam & Hullman, Jessica & Hofman, Jake M. & Goldstein, Daniel G., 2025. "Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping," International Journal of Forecasting, Elsevier, vol. 41(2), pages 689-701.
    • Handle: RePEc:eee:intfor:v:41:y:2025:i:2:p:689-701
    DOI: 10.1016/j.ijforecast.2024.07.002
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